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Tables are widely used in various kinds of documents to present informationconcisely. Understanding tables is a challenging problem that requires anunderstanding of language and table structure, along with numerical and logicalreasoning. In this paper, we present our systems to solve Task 9 ofSemEval-2021: Statement Verification and Evidence Finding with Tables(SEM-TAB-FACTS). The task consists of two subtasks: (A) Given a table and astatement, predicting whether the table supports the statement and (B)Predicting which cells in the table provide evidence for/against the statement.We fine-tune TAPAS (a model which extends BERT's architecture to capturetabular structure) for both the subtasks as it has shown state-of-the-artperformance in various table understanding tasks. In subtask A, we evaluate howtransfer learning and standardizing tables to have a single header row improvesTAPAS' performance. In subtask B, we evaluate how different fine-tuningstrategies can improve TAPAS' performance. Our systems achieve an F1 score of67.34 in subtask A three-way classification, 72.89 in subtask A two-wayclassification, and 62.95 in subtask B.

Volta at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables using TAPAS and Transfer Learning